Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)
In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
PloS one - 15(2020), 5 vom: 04., Seite e0232127 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Xia [VerfasserIn] |
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| Links: |
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| Themen: |
Comparative Study |
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| Anmerkungen: |
Date Completed 27.07.2020 Date Revised 27.07.2020 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0232127 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM309487188 |
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| 245 | 1 | 0 | |a Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet) |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature | ||
| 650 | 4 | |a Comparative Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Shen, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yongxia |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiuhui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tie-Qiang |e verfasserin |4 aut | |
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